Patient motion such as respiratory or cardiac motion can degrade the image quality and quantitative accuracy of imaging studies such as cardiac SPECT studies. For example, motion blur can lead to an underestimation of SPECT activity and/or to other image artifacts. Such motion has been compensated using motion-compensated SPECT reconstruction. Conventional motion-compensation approaches require an estimation of a motion vector field or motion model of the region of interest.
A simple motion model has been estimated by tracking statistical features like the center of mass of the SPECT activity in SPECT projection data. However, this method yields a very crude model and can only track motion parallel to the plane of each SPECT projection. Alternatively, a statistical model can be used that represents a mean motion pattern averaged over a patient. Unfortunately, such an approach cannot capture patient-specific motion patterns. A more sophisticated motion model can be derived from dynamic CT, e.g., by performing gated reconstruction (respiratory or cardiac) for different motion states and applying elastic registration. However, dynamic CT data may not always be available, and such studies add to the total dose deposited to the patient for the imaging examination.
Another approach employs external devices, such as video cameras, to track and determine respiratory motion. Such approaches typically face two challenges: 1) they only track the motion of the body surface, and there is no direct link between the external motion and the actual non-rigid motion of internal organs; and 2) they add additional cost to the system and consume additional space. Approaches aiming at directly estimating motion from gated SPECT reconstructions may be hindered by the low count statistics and poor image quality of such reconstructions.
In view of the foregoing, there is an unresolved need for other motion compensation approaches.